Interdisciplinary research is considered a source of innovativeness and creativity, serving as a key mechanism for creating recombination necessary for the evolution of science systems. The aim of this study is to quantitatively establish the connection between interdisciplinary research and the research fronts that have recently emerged in civil engineering. The degree of interdisciplinarity of the research fronts was measured by developing metrics from bibliographic analyses. As indicated by the consistent increase in the metrics of interdisciplinarity over time, research fronts tend to emerge in studies with increasing diversity in the disciplines involved. The active disciplines involved in the fronts vary over time. The most active disciplines are no longer fundamental but those associated with energy, environment, and sustainable development, focusing on solutions to climate change and integrating intelligence technologies.

Improving the cracking resistance of steel-normal concrete (NC) composite beams in the negative moment region is one of the main tasks in designing continuous composite beam (CCB) bridges due to the low tensile strength of the NC deck at pier supports. This study proposed an innovative structural configuration for the negative bending moment region in a steel-concrete CCB bridge with the aid of ultrahigh performance concrete (UHPC) layer. In order to investigate the feasibility and effectiveness of this new UHPC jointed structure in the negative bending moment region, field load testing was conducted on a newly built full-scale bridge. The newly designed structural configuration was described in detail regarding the structural characteristics (cracking resistance, economy, durability, and constructability). In the field investigation, strains on the surface of the concrete bridge deck, rebar, and steel beam in the negative bending moment region, as well as mid-span deflection, were measured under different load cases. Also, a finite element model for the four-span superstructure of the full-scale bridge was established and validated by the field test results. The simulated results in terms of strains and mid-span deflection showed moderate consistency with the test results. This field test and the finite element model results demonstrated that the new configuration with the UHPC layer provided an effective alternative for the negative bending moment region of the composite beam.

In this study, the flexural and longitudinal shear performances of two types of precast lightweight steel–ultra-high performance concrete (UHPC) composite beams are investigated, where a cluster UHPC slab (CUS) and a normal UHPC slab (NUS) are connected to a steel beam using headed studs through discontinuous shear pockets and full-length shear pockets, respectively. Results show that the longitudinal shear force of the CUS is greater than that of the NUS, whereas the interfacial slip of the former is smaller. Owing to its better integrity, the CUS exhibits greater flexural stiffness and a higher ultimate bearing capacity than the NUS. To further optimize the design parameters of the CUS, a parametric study is conducted to investigate their effects on the flexural and longitudinal shear performances. The square shear pocket is shown to be more applicable for the CUS, as the optimal spacing between two shear pockets is 650 mm. Moreover, a design method for transverse reinforcement is proposed; the transverse reinforcement is used to withstand the splitting force caused by studs in the shear pocket and prevent the UHPC slab from cracking. According to calculation results, the transverse reinforcement can be canceled when the compressive strength of UHPC is 150 MPa and the volume fraction of steel fiber exceeds 2.0%.

This paper presents a study of closely spaced double tunnels in Taizhou, China. One is Xiabei Mountain No. 2 four-line super-span high-speed railway tunnel (HRT), and the other is Xiabei Mountain double-line large-span subway tunnel (ST). The excavation spans of HRT and ST are 26.3 and 14 m, respectively. The two tunnels are located at different levels, and their separating distance is 17.2 m. Due to the short construction period, the HRT excavation was completed earlier than ST. The structural design of the HRT, taking account of the disturbance by the ST construction, was analyzed by a numerical simulation. It was found that the “yielding principle” design was more feasible than the “resistance principle” design when considering the safety and durability of the HRT secondary lining. The mechanical responses of the HRT during ST construction were comprehensively monitored and analyzed, including the vault settlement, horizontal convergence, surrounding rock pressure, and the internal stress in shotcrete and steel arch. Results show that the longitudinal influence range of the ST construction on the HRT was approximately 0.6–1.1 times the ST outer diameter; the disturbance was mainly generated in the ST upper bench excavation; and the final axial force of the HRT shotcrete was approximately 9–16 times that of the steel arch, which indicated that the shotcrete was the main bearing structure. The safety status of the HRT was assessed based on the monitoring data, and the minimum safety factors of the HRT shotcrete and steel arch were 1.61 and 1.89, respectively. Parametric studies were performed to show how the lining stress of HRT was affected by the relative angle, pillar width, ST excavation method and excavation footage. Finally, the design and construction optimization were proposed according to the monitoring data and parameter analysis results. This study might provide practical reference for similar projects.

In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

This study focuses on the bending failure performance of a shield tunnel segment. A full-scale test was conducted to investigate deformation and failure characteristics. During the loading, the bending failure process can be divided into four stages: the elastic stage, working stage with cracks, failure stage, and ultimate stage. The characteristic loads between contiguous stages are the cracking, failure, and ultimate loads. A numerical model corresponding to the test was established using the elastoplastic damage constitutive model of concrete. After a comparative analysis of the simulation and test results, parametric studies were performed to discuss the influence of the reinforcement ratio and proportion of tensile longitudinal reinforcement on the bearing capacity. The results indicated that the change in the reinforcement ratio and the proportion of tensile longitudinal reinforcement had little effect on the cracking load but significantly influenced the failure and ultimate loads of the segment. It is suggested that in the reinforcement design of the subway segment, the reinforcement ratio and the proportion of tensile longitudinal reinforcement can be chosen in the range of 0.7%–1.2% and 49%–55%, respectively, allowing the segment to effectively use the reinforcement and exert the design strength, thereby improving the bearing capacity of the segment.

Experimental evaluations were conducted to determine the water sorptivity, setting time, and resistance to a highly acidic environment, of mortar with alkali-activated ground granulated blast furnace slag (GBS) binder and also of combinations of fly ash and GBS binders. Binders were activated using mixtures of NaOH and Na₂SiO₃ solutions. The molarity of NaOH in the mixtures ranged from 10 mol·L⁻¹ to 16 mol·L⁻¹, and the Na₂SiO₃/NaOH ratio was varied from 1.5 to 2.5. Mortar samples were produced using three binder combinations: 1) GBS as the only binder; 2) blended binder with a slag-to-fly ash ratio of 3:1; and 3) mixed binder with 1:1 ratio of slag to fly ash. Mortar samples were mixed and cured at (22 ± 2) °C till the day of the test. The impact of activator solution alkalinity, activator ratio Na₂SiO₃/NaOH, GBS content on the rate of water absorption were evaluated. After 7, 28, and 90 d of immersion in a 10% sulfuric acid solution, the resistance of a geopolymer matrix to degradation was assessed by measuring the change in sample weight. The influence of solution alkalinity and relative fly ash content on setting times was investigated. Alkali-activated mortar with a slag-to-fly ash ratio of 3:1 had the least sorptivity compared to the two other binder combinations, at each curing age, and for mortars made with each of the NaOH alkaline activator concentrations. Mortar sorptivity decreased with age and sodium hydroxide concentrations, suggesting the production of geopolymerization products. No reduction in weight of sample occurred after immersion in the strong acid H₂SO₄ solution for three months, regardless of binder combination. This was due to the synthesis of hydration and geopolymerization products in the presence of curing water, which outweighed the degradation of the geopolymer matrix caused by sulfuric acid.

Three-point bending tests were carried out on nineteen Reinforced Concrete (RC) beams strengthened with FRP in the form of completely wrapping. The strip width to spacing ratios, FRP type, shear span to effective depth ratios, the number of FRP layers in shear, and the effect of stirrups spacing were the parameters investigated in the experimental study. The FRP contribution to strength on beams having the same strip width to spacing ratios could be affected by the shear span to effective depth ratios and stirrups spacing. The FRP contributions to strength were less on beams with stirrups in comparison to the tested beams without stirrups. Strengthening RC beams using FRP could change the failure modes of the beams compared to the reference beam. In addition to the experimental study, a number of equations used to predict the FRP contribution to the shear strength of the strengthened RC beams were assessed by using a limited number of beams available in the literature. The effective FRP strain is predicted by using test results, and this prediction is used to calculate the FRP contribution to shear strength in ACI 440.2R (2017) equation. Based on the statistical values of the data, the proposed equation has the lowest coefficient of variation (COV) value than the other equations.

The interactions between reinforced concrete (RC) frames and infill walls play an important role in the seismic response of frames, particularly for low-rise frames. Infill walls can increase the overall lateral strength and stiffness of the frame owing to their high strength and stiffness. However, local wall-frame interactions can also lead to increased shear demand in the columns owing to the compressive diagonal strut force from the infill wall, which can result in failure or in serious situations, collapse. In this study, the effectiveness of a design strategy to consider the complex infill wall interaction was investigated. The approach was used to design example RC frames with infill walls in locations with different seismicity levels in Thailand. The performance of these frames was assessed using nonlinear static, and dynamic analyses. The performance of the frames and the failure modes were compared with those of frames designed without considering the infill wall or the local interactions. It was found that even though the overall responses of the buildings designed with and without consideration of the local interaction of the infill walls were similar in terms the overall lateral strength, the failure modes were different. The proposed method can eliminate the column shear failure from the building. Finally, the merits and limitations of this approach are discussed and summarized.

Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressive strength (CS) is a critical mechanical property that must be adequately determined. In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include the limitations of samples in databases, the applicability constraints of models owing to limited mixture components, and the possibility of applying recently proposed models. This study developed different ML models for predicting the CS of FRSCC to address these limitations. Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique. A database of 381 samples was created, representing the most significant FRSCC dataset compared with previous studies, and it was used for model development. The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities (root mean square error of 2.639 MPa, mean absolute error of 1.669 MPa, and coefficient of determination of 0.986 for the test dataset). Finally, a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC. The results showed that the cement content, testing age, and superplasticizer content are the most critical factors affecting the CS.

The stress state of the built-in corridor in core rock-fill dam on thick overburden is extremely complex, which may produce cracking and damage. The purpose of this paper was to investigate the effect of thick overburden on the stress and deformation of the built-in corridor in a rock-fill dam, and ascertain the damage causes of the corridor. The rationality of the analysis method for corridor with similar structure is another focus. The approach is based on finite-element method and the calculation result accuracy is verified by the field monitoring data. The improved analysis method for corridors with similar structure is proposed by comparing various corridor load calculation methods and concrete constitutive models. Results demonstrate that the damage causes of the corridor are the deformability difference between the overburden and concrete and the special structural form. And the calculation model considering dam construction process, contact between concrete and surrounding soil, and concrete damage plasticity can reasonably reflect the mechanical behavior of the corridor. The research conclusions may have a reference significance for the analysis of tunnels similar to built-in corridors.

An algorithm based on deep semantic segmentation called LC-DeepLab is proposed for detecting the trends and geometries of cracks on tunnel linings at the pixel level. The proposed method addresses the low accuracy of tunnel crack segmentation and the slow detection speed of conventional models in complex backgrounds. The novel algorithm is based on the DeepLabv3+ network framework. A lighter backbone network was used for feature extraction. Next, an efficient shallow feature fusion module that extracts crack features across pixels is designed to improve the edges of crack segmentation. Finally, an efficient attention module that significantly improves the anti-interference ability of the model in complex backgrounds is validated. Four classic semantic segmentation algorithms (fully convolutional network, pyramid scene parsing network, U-Net, and DeepLabv3+) are selected for comparative analysis to verify the effectiveness of the proposed algorithm. The experimental results show that LC-DeepLab can accurately segment and highlight cracks from tunnel linings in complex backgrounds, and the accuracy (mean intersection over union) is 78.26%. The LC-DeepLab can achieve a real-time segmentation of 416 × 416 × 3 defect images with 46.98 f/s and 21.85 Mb parameters.

To realize seismic-resilient reinforced concrete (RC) moment-resisting frame structures, a novel self-centering RC column with a rubber layer placed at the bottom (SRRC column) is proposed herein. For the column, the longitudinal reinforcement dissipates seismic energy, the rubber layer allows the rocking of the column, and the unbonded prestressed tendon enables self-centering capacity. A refined finite element model of the SRRC column is developed, the effectiveness of which is validated based on experimental results. Results show that the SRRC column exhibits stable energy dissipation capacity and no strength degradation; additionally, it can significantly reduce permanent residual deformation and mitigate damage to concrete. Extensive parametric studies pertaining to SRRC columns have been conducted to investigate the critical factors affecting their seismic performance.

The present study describes a reliability analysis of the strength model for predicting concrete columns confinement influence with Fabric-Reinforced Cementitious Matrix (FRCM). through both physical models and Deep Neural Network model (artificial neural network (ANN) with double and triple hidden layers). The database of 330 samples collected for the training model contains many important parameters, i.e., section type (circle or square), corner radius r_c, unconfined concrete strength f_co, thickness n_t, the elastic modulus of fiber E_f , the elastic modulus of mortar E_m. The results revealed that the proposed ANN models well predicted the compressive strength of FRCM with high prediction accuracy. The ANN model with double hidden layers (APDL-1) was shown to be the best to predict the compressive strength of FRCM confined columns compared with the ACI design code and five physical models. Furthermore, the results also reveal that the unconfined compressive strength of concrete, type of fiber mesh for FRCM, type of section, and the corner radius ratio, are the most significant input variables in the efficiency of FRCM confinement prediction. The performance of the proposed ANN models (including double and triple hidden layers) had high precision with R higher than 0.93 and RMSE smaller than 0.13, as compared with other models from the literature available.

This paper numerically studied the effect of uncertainty and random distribution of concrete strength in beams failing in shear and flexure using lattice modeling, which is suitable for statistical analysis. The independent variables of this study included the level of strength reduction and the number of members with reduced strength. Three levels of material deficiency (i.e., 10%, 20%, 30%) were randomly introduced to 5%, 10%, 15%, and 20% of members. To provide a database and reliable results, 1000 analyses were carried out (a total of 24000 analyses) using the MATLAB software for each combination. Comparative studies were conducted for both shear- and flexure-deficit beams under four-point loading and results were compared using finite element software where relevant. Capability of lattice modeling was highlighted as an efficient tool to account for uncertainty in statistical studies. Results showed that the number of deficient members had a more significant effect on beam capacity compared to the level of strength deficiency. The scatter of random load-capacities was higher in flexure (range: 0.680–0.990) than that of shear (range: 0.795–0.996). Finally, nonlinear regression relationships were established with coefficient of correlation values (R²) above 0.90, which captured the overall load–deflection response and level of load reduction.

The integrity and bearing capacity of segment joints in shield tunnels are associated closely with the mechanical properties of the joints. This study focuses on the mechanical characteristics and mechanism of a bolted circumferential joint during the entire bearing process. Simplified analytical algorithms for four stress stages are established to describe the bearing behaviors of the joint under a compressive bending load. A height adjustment coefficient, α, for the outer concrete compression zone is introduced into a simplified analytical model. Factors affecting α are determined, and the degree of influence of these factors is investigated via orthogonal numerical simulations. The numerical results show that α can be specified as approximately 0.2 for most metro shield tunnels in China. Subsequently, a case study is performed to verify the rationality of the simplified theoretical analysis for the segment joint via numerical simulations and experiments. Using the proposed simplified analytical algorithms, a parametric investigation is conducted to discuss the factors affecting the ultimate compressive bending capacity of the joint. The method for optimizing the joint flexural stiffness is clarified. The results of this study can provide a theoretical basis for optimizing the design and prediciting the damage of bolted segment joints in shield tunnels.

This paper addresses the potential use of Sugar Cane Bagasse Ash (SCBA) as a pozzolanic material for partial cement replacement in concrete mixtures. Cement mortars containing SCBA having five different particle size distributions at a replacement rate of 20% by weight were used to study the chemical and physical pozzolanic properties of SCBA. The durability of SCBA replaced mortars was also evaluated. SCBA with 0% retained on sieve No. 325 was used to replace 20% by weight of cement and create mortar specimens that were subjected to sulfuric acid attack of varying concentrations (1%−3% by weight of water). The tested samples were observed to check visual distortion, mass loss, and compressive strength loss at 1, 7, 14, 28, and 56 d of acidic exposure, and the results were compared to those for the control sample, that was lime water cured, at the same ages. The SCBA sets were found to meet the requirements for pozzolan class N specified by ASTM C 618. Mortars containing SCBA with 0% or 15% retention produced better compressive strength than the control mortars after 28 d. Additionally, X-ray fluorescence and X-ray diffraction analysis showed that the SCBA had favorable chemical properties for a pozzolanic material. Furthermore, SCBA replaced samples at all ages showed improved resistance against acidic attack relative to that of the control mortars. Maximum deterioration was seen for 3% concentrated solution. This study’s findings demonstrated that SCBA with an appropriate fineness could be used as a pozzolanic material, consistently with ASTM C 618.

The prediction of structural performance plays a significant role in damage assessment of glass fiber reinforcement polymer (GFRP) elastic gridshell structures. Machine learning (ML) approaches are implemented in this study, to predict maximum stress and displacement of GFRP elastic gridshell structures. Several ML algorithms, including linear regression (LR), ridge regression (RR), support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), category boosting (CatBoost), and light gradient boosting machine (LightGBM), are implemented in this study. Output features of structural performance considered in this study are the maximum stress as f₁(x) and the maximum displacement to self-weight ratio as f₂(x). A comparative study is conducted and the Catboost model presents the highest prediction accuracy. Finally, interpretable ML approaches, including shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions. SHAP is employed to describe the importance of each variable to structural performance both locally and globally. The results of sensitivity analysis (SA), feature importance of the CatBoost model and SHAP approach indicate the same parameters as the most significant variables for f₁(x) and f₂(x).

Blasting engineering in complex urban environments is considered to influence the safety and stability of the overlying drainage box culvert structure owing to vibration. Therefore, field blasting and vibration tests were performed on the blasting engineering of the Wuhan Metro Line 8 connected aisle, and the LS-DYNA software was used to analyze the dynamic response characteristics of an underground drainage box culvert during the blasting test. The vibration response evolution law of the buried drainage box culvert under blasting vibration was investigated, and a safe surface control standard for the blast vibration of a drainage box culvert is proposed. The results reveal that the maximum tensile stress of the box culvert structure was 0.33 MPa. The peak particle velocity (PPV) and peak tensile stress (PTS) of the drainage box culvert decreased as the water level in the box culvert increased. Based on the relationship between the tensile stress of the box culvert, PPV of the box culvert, and PPV of the surface, it is proposed that the surface control velocity of the buried drainage box culvert is 1.36 cm/s.

Reinforced concrete structural walls are commonly used for resisting lateral forces in buildings. Owing to the advancements in the field of concrete materials over the past few decades, concrete mixes of high compressive strength, commonly referred to as high-strength concrete (HSC), have been developed. In this study, the effects of strategic placement of HSC on the performance of slender walls were examined. The finite-element model of a conventional normal-strength concrete (NSC) prototype wall was validated using test data available in extant studies. HSC was incorporated in the boundary elements of the wall to compare its performance with that of the conventional wall at different axial loads. Potential reductions in the reinforcement area and size of the boundary elements were investigated. The HSC wall exhibited improved strength and stiffness, and thereby, allowed reduction in the longitudinal reinforcement area and size of the boundary elements for the same strength of the conventional wall. Cold joints resulting from dissimilar concrete pours in the web and boundary elements of the HSC wall were modeled and their impact on behavior of the wall was examined.

The objective of the current study is to propose an expert system framework based on a supervised machine learning technique (MLT) to predict the seismic performance of low- to mid-rise frame structures considering soil-structure interaction (SSI). The methodology of the framework is based on examining different MLTs to obtain the highest possible accuracy for prediction. Within the MLT, a sensitivity analysis was conducted on the main SSI parameters to select the most effective input parameters. Multiple limit state criteria were used for the seismic evaluation within the process. A new global seismic assessment ratio was introduced that considers both serviceability and strength aspects by utilizing three different engineering demand parameters (EDPs). The proposed framework is novel because it enables the designer to seismically assess the structure, while simultaneously considering different EDPs and multiple limit states. Moreover, the framework provides recommendations for building component design based on the newly introduced global seismic assessment ratio, which considers different levels of seismic hazards. The proposed framework was validated through comparison using non-linear time history (NLTH) analysis. The results show that the proposed framework provides more accurate results than conventional methods. Finally, the generalization potential of the proposed framework was tested by investigating two different types of structural irregularities, namely, stiffness and mass irregularities. The results from the framework were in good agreement with the NLTH analysis results for the selected case studies, and peak ground acceleration (PGA) was found to be the most influential input parameter in the assessment process for the case study models investigated. The proposed framework shows high generalization potential for low- to mid-rise structures.

The present study proposes the mix design method of Fly Ash (FA) based geopolymer concrete using Response Surface Methodology (RSM). In this method, different factors, including binder content, alkali/binder ratio, NS/NH ratio (sodium silicate/sodium hydroxide), NH molarity, and water/solids ratio were considered for the mix design of geopolymer concrete. The 2D contour plots were used to setup the mix design method to achieve the target compressive strength. The proposed mix design method of geopolymer concrete is divided into three categories based on curing regime, specifically one ambient curing (25 °C) and two heat curing (60 and 90 °C). The proposed mix design method of geopolymer concrete was validated through experimentation of M30, M50, and M70 concrete mixes at all curing regimes. The observed experimental compressive strength results validate the mix design method by more than 90% of their target strength. Furthermore, the current study concluded that the required compressive strength can be achieved by varying any factor in the mix design. In addition, the factor analysis revealed that the NS/NH ratio significantly affects the compressive strength of geopolymer concrete.

This study modeled the moving-vehicle-induced forcing excitation on a single-span prismatic bridge as a multiple frequency-multiplication harmonic load on the modal coordinates of a linear elastic simple Euler–Bernoulli beam, and investigated the forced modal oscillation and resonance behavior of this type of dynamic system. The forced modal responses consist of multiple frequency-multiplication steady-state harmonics and one damped mono-frequency complementary harmonic. The analysis revealed that a moving load induces high-harmonic forced resonance amplification when the moving speed is low. To verify the occurrence of high-harmonic forced resonance, numerical tests were conducted on single-span simple beams based on structural modeling using the finite element method (FEM) and a moving sprung-mass oscillator vehicle model. The forced resonance amplification characteristics of the fundamental mode for beam response estimation are presented with consideration to different end restraint conditions. The results reveal that the high-harmonic forced resonance may be significant for the investigated beams subjected to vehicle loads moving at specific low speeds. For the investigated single-span simple beams, the moving vehicle carriage heaving oscillation modulates the beam modal frequency, but does not induce notable variation of the modal oscillation harmonic structure for the cases that vehicle of small mass moves in low speed.

An approach to control the profiles of interstory drift ratios along the height of building structures via topology optimization is proposed herein. The theoretical foundation of the proposed approach involves solving a min–max optimization problem to suppress the maximum interstory drift ratio among all stories. Two formulations are suggested: one inherits the bound formulation and the other utilizes a p-norm function to aggregate all individual interstory drift ratios. The proposed methodology can shape the interstory drift ratio profiles into inverted triangular or quadratic patterns because it realizes profile control using a group of shape weight coefficients. The proposed formulations are validated via a series of numerical examples. The disparity between the two formulations is clear. The optimization results show the optimal structural features for controlling the interstory drift ratios under different requirements.

The aim of this study is to appraise the potential of calcium sulfoaluminate (CSA) cement-based grouts in simulated permafrost environments. The hydration and performance of CSA cement-based grouts cured in cold environments (10, 0, and −10 °C) are investigated using a combination of tests, including temperature recording, X-ray diffraction (XRD) tests, thermogravimetric analysis (TGA), and unconfined compressive strength (UCS) tests. The recorded temperature shows a rapid increase in temperature at the early stage in all the samples. Meanwhile, results of the TGA and XRD tests show the generation of a significant quantity of hydration products, which indicates the rapid hydration of CSA cement-based grouts at the early stage at low temperatures. Consequently, the CSA cement-based grouts exhibit remarkably high early strength. The UCS values of the samples cured for 2 h at −10, 0, and 10 °C are 6.5, 12.0, and 12.3 MPa, respectively. The UCS of the grouts cured at −10, 0, and 10 °C increases continuously with age and ultimately reached 14.9, 19.0, and 30.6 MPa at 28 d, respectively. The findings show that the strength of grouts fabricated using CSA cement can develop rapidly in cold environments, thus rendering them promising for permafrost applications.

A numerical framework was proposed for the seismic analysis of underground structures in layered ground under inclined P-SV waves. The free-field responses are first obtained using the stiffness matrix method based on plane-wave assumptions. Then, the domain reduction method was employed to reproduce the wavefield in the numerical model of the soil–structure system. The proposed numerical framework was verified by providing comparisons with analytical solutions for cases involving free-field responses of homogeneous ground, layered ground, and pressure-dependent heterogeneous ground, as well as for an example of a soil–structure interaction simulation. Compared with the viscous and viscous-spring boundary methods adopted in previous studies, the proposed framework exhibits the advantage of incorporating oblique incident waves in a nonlinear heterogeneous ground. Numerical results show that SV-waves are more destructive to underground structures than P-waves, and the responses of underground structures are significantly affected by the incident angles.

In the present study, a comparison between the new shallow tunneling method (STM) and the traditional pile and rib method (PRM) was conducted to excavate and construct subway stations in the geological conditions of Tehran. First, by selecting Station Z6 located in the Tehran Subway Line 6 as a case study, the construction process was analyzed by PRM. The maximum ground settlement of 29.84 mm obtained from this method was related to the station axis, and it was within the allowable settlement limit of 30 mm. The acceptable agreement between the results of numerical modeling and instrumentation data indicated the confirmation and accuracy of the excavation and construction process of Station Z6 by PRM. In the next stage, based on the numerical model validated by instrumentation data, the value of the ground surface settlement was investigated during the station excavation and construction by STM. The results obtained from STM showed a significant reduction in the ground surface settlement compared to PRM. The maximum settlement obtained from STM was 6.09 mm as related to the front of the excavation face. Also, the sensitivity analysis results denoted that in addition to controlling the surface settlement by STM, it is possible to optimize some critical geometric parameters of the support system during the station excavation and construction.

The study proposes a framework combining machine learning (ML) models into a logical hierarchical system which evaluates the stability of the sheet wall before other predictions. The study uses the hardening soil (HS) model to develop a 200-sample finite element analysis (FEA) database, to develop the ML models. Consequently, a system containing three trained ML models is proposed to first predict the stability status (random forest classification, RFC) followed by 1) the cantilever top horizontal displacement of sheet wall (artificial neural network regression models, RANN1) and 2) vertical settlement of soil (RANN2). The uncertainty of this data-driven system is partially investigated by developing 1000 RFC models, based on the application of random sampling technique in the data splitting process. Investigation on the distribution of the evaluation metrics reveals negative skewed data toward the 1.0000 value. This implies a high performance of RFC on the database with medians of accuracy, precision, and recall, on test set are 1.0000, 1.0000, and 0.92857, respectively. The regression ANN models have coefficient of determinations on test set, as high as 0.9521 for RANN1, and 0.9988 for RANN2, respectively. The parametric study for these regressions is also provided to evaluate the relative insight influence of inputs to output.

The tunnel boring machine (TBM) is typically used in hard-rock tunnel excavation. Owing to the unsatisfactory adaptability of TBM to the surrounding rock, when crossing high-strength and high-wear strata, the TBM can easily cause defects, such as abnormal wear on cutters and overload damage to bearings, thus affecting the construction efficiency and cost. Therefore, high-pressure waterjet technology should be applied to assist in rock breaking for efficient TBM tunneling. In this study, the effects of water pressure, nozzle diameter, and nozzle speed on cutting are investigated via laboratory experiments of cutting hard rock using high-pressure waterjets. The penetration performance of the TBM under different water pressures is investigated via a field industrial penetration test. The results show that high-pressure waterjets are highly efficient for rock breaking and are suitable for industrial applications, as they can accommodate the advancing speed of the TBM and achieve high-efficiency rock breaking. However, during the operation of high-pressure waterjets, the ambient temperature and waterjet temperature in the tunnel increase significantly, which weakens the cooling effect of the cutterhead and decreases the construction efficiency of the TBM. Therefore, temperature control and cooling measures for high-pressure waterjets during their long-term operation must be identified. This study provides a useful reference for the design and construction of high-pressure water-jet-assisted cutterheads for breaking road headers.

This paper presents a novel approach for simulating the localized leakage behavior of segmentally lined tunnels based on a cohesive zone model. The proposed approach not only simulates localized leakage at the lining segment, but also captures the hydromechanically coupled seepage behavior at the segmental joints. It is first verified via a tunnel drainage experiment, which reveals its merits over the existing local hydraulic conductivity method. Subsequently, a parametric study is conducted to investigate the effects of the aperture size, stratum permeability, and spatial distribution of drainage holes on the leakage behavior, stratum seepage field, and leakage-induced mechanical response of the tunnel lining. The proposed approach yields more accurate results than the classical local hydraulic conductivity method. Moreover, it is both computationally efficient and stable. Localized leakage leads to reduced local ground pressure, which further induces outward deformation near the leakage point and slight inward deformation at its diametrically opposite side. A localized stress arch spanning across the leakage point is observed, which manifests as the rotation of the principal stresses in the adjacent area. The seepage field depends on both the number and location of the leakage zones. Pseudostatic seepage zones, in which the seepage rate is significantly lower than that of the adjacent area, appear when multiple seepage zones are considered. Finally, the importance of employing the hydromechanical coupled mechanism at the segment joints is highlighted by cases of shallowly buried tunnels subjected to surface loading and pressure tunnels while considering internal water pressure.